What the data for this category looks like
Survey and design units provide data from their project management systems and submission archiving systems. Materials are submitted on demand during each project submission phase, with no fixed update cycle. Documents include two types of content: structured submission forms such as the project basic information form and survey scope form, and unstructured documents including Word survey reports, PDF construction drawings, and CAD drawing files. Fields include standardized content such as project number, survey depth, floor area, and review opinion number. Some drawings have custom annotation units, and there are certain format differences.
What constraints do these characteristics impose on the model access and configuration link
Mixed structured and unstructured data creates specific requirements. Models must support multimodal input and format parsing. Corresponding multimodal parameters must be configured. A single declaration material may include dozens of survey report pages and multiple CAD drawings. Total token counts are high. Deployed models must adapt to long context windows. File review requires strict matching of fixed fields and formats. Model output must have high rigor. Overly high randomness parameters should not be used. Multimodal material parsing increases token consumption. Administrators must set reasonable token limits to avoid request interruptions.
How to Configure
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | The total token count of long documents such as survey reports and construction drawings usually falls within this range, and can fully load all content required for declaration materials and file review |
maxTokens | 2000–3000 token | A single review result needs to cover compliance judgments and modification suggestions. This range avoids truncation of key information in the output |
temperature | 0.1–0.3 | File review requires rigorous, fixed-format output. Low temperature reduces output randomness and improves field matching accuracy |
vision | Enabled | Declaration materials include multimodal content such as CAD construction drawings and survey site photos, requiring the model to support image parsing capabilities |
toolChoice | auto | Logic such as field extraction and format verification needs to be called on demand during the review process. Automatic mode can adapt to complex review scenarios |
AIPROXY_API_ENDPOINT | Official access address of the corresponding model service provider | Ensures correct routing of model requests and avoids errors caused by mismatched request paths |
Three Common Configuration Mistakes
- Phenomenon: When
AIPROXY_API_ENDPOINTis configured with a non-official address, a 404 error is returned. Cause: The routing address for model access is configured incorrectly, and the service provider's API path cannot be matched correctly. - Phenomenon: When
temperatureis set higher than 0.5, classification results vary significantly, and some fields have incorrect matching. Cause: High temperature increases the randomness of model output, which cannot meet the rigor requirements of format and content for file review. - Phenomenon: The
maxContextandmaxTokensparameters are not adjusted, and timeouts occur when processing materials containing multiple CAD drawings. Cause: The token consumption requirements of multimodal materials are not adapted, and default parameters cannot cover the parsing overhead of long texts and images.
How to Confirm the Configuration is Complete
- Submit a standard survey declaration material, check whether the review result returned by the model includes correct fields such as project number and survey depth.
- View the model call logs in FastGPT v4.17.0, confirm that the request address of
AIPROXY_API_ENDPOINTmatches the official address of the service provider, and no 404 status code is returned. - Upload test materials containing CAD screenshots, confirm that the model can correctly identify annotation information and dimension data in the drawings.
- Adjust the
temperatureparameter to 0.2, compare the differences between the two output results, and confirm that the output stability meets the review requirements.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.